# An Empirical Investigation of V-I Trajectory based Load Signatures for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/1305.0596 · HTML: https://mlsanity.com/paper/1305.0596/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2013.

## Built on by (most cited first, 2 of 2)

- [Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning](https://mlsanity.com/paper/2209.03759/) (2022)
- [Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring](https://mlsanity.com/paper/2208.10638/) (2022)

## Abstract (arXiv)

Choice of load signature or feature space is one of the most fundamental design choices for non-intrusive load monitoring or energy disaggregation problem. Electrical power quantities, harmonic load characteristics, canonical transient and steady-state waveforms are some of the typical choices of load signature or load signature basis for current research addressing appliance classification and prediction. This paper expands and evaluates appliance load signatures based on V-I trajectory - the mutual locus of instantaneous voltage and current waveforms - for precision and robustness of prediction in classification algorithms used to disaggregate residential overall energy use and predict constituent appliance profiles. We also demonstrate the use of variants of differential evolution as a novel strategy for selection of optimal load models in context of energy disaggregation. A publicly available benchmark dataset REDD is employed for evaluation purposes. Our experimental evaluations indicate that these load signatures, in conjunction with a number of popular classification algorithms, offer better or generally comparable overall precision of prediction, robustness and reliability against dynamic, noisy and highly similar load signatures with reference to electrical power quantities and harmonic content. Herein, wave-shape features are found to be an effective new basis of classification and prediction for semi-automated energy disaggregation and monitoring.